1. Bioinformatics for high-throughput DNA sequencing Gabor Marth Boston College Biology New grad student orientation Boston College September 8, 2009

2. DNA sequence variations The Human Genome Project has determined a reference sequence of the human genome However, every individual is unique, and is different from others at millions of nucleotide locations

3. Why do we care about variations? underlie phenotypic differences cause inherited diseases allow tracking ancestral human history

4. 4 Human genetic variation

5. The first “famous” genomes

6. Genome sequencing ~1 Mb ~100 Mb>100 Mb~3,000 Mb

7. New sequencing technologies…

8. Next-gen sequencing – a revolution read length bases per machine run 10 bp1,000 bp100 bp 1 Gb 100 Mb 10 Mb 10 Gb Illumina/Solexa, AB/SOLiD sequencers ABI capillary sequencer Roche/454 pyrosequencer (100-400 Mb in 200-450 bp reads) (10-30Gb in 25-100 bp reads) 1 Mb 100 Gb

9. The re-sequencing informatics pipeline REF (ii) read mapping IND (i) base calling IND (iii) SNP and short INDEL calling (v) data viewing, hypothesis generation (iv) SV calling

10. Tools

11. Read mapping is like a jigsaw puzzle… … and they give you the picture on the box 2. Read mapping …you get the pieces… Big and Unique pieces are easier to place than others…

12. The MOSAIK read mapping program Reads from repeats cannot be uniquely mapped back to their true region of origin Michael Strömberg (Wan-Ping Lee)

13. SNP discovery Marth et al. Nature Genetics 1999 Quinlan et al. in prep. (Amit Indap, Wen Fung Leong)

14. Structural variation discovery Navigation bar Fragment lengths in selected region Depth of coverage in selected region Stewart et al. in prep. (Deniz Kural, Jiantao Wu)

15. Sequence alignment viewers Huang et al. Genome Research 2008 (Derek Barnett)

16. Data mining

17. Mutational profiling in deep 454 data Pichia stipitis is a yeast that efficiently converts xylose to ethanol (bio-fuel production) one specific mutagenized strain had especially high conversion efficiency goal was to determine where the mutations were that caused this phenotype we analyzed 10 runs (~3 million reads) of 454 reads (~20x coverage of the 15MB genome) Pichia stipitis reference sequence found 39 mutations informatics analysis in < 24 hours (including manual checking of all candidates) Image from JGI web site Smith et al. Genome Research 2008

18. SNP calling in short-read coverage C. elegans reference genome (Bristol, N2 strain) Pasadena, CB4858 (1 ½ machine runs) Bristol, N2 strain (3 ½ machine runs) goal was to evaluate the Solexa/Illumina technology for the complete resequencing of large model-organism genomes 5 runs (~120 million) Illumina reads were collected by Washington Univ. SNP we found 45,000 SNP with very high validation rate Hillier et al. Nature Methods 2008

19. Current focus

20. 1000 Genomes Project data quality assessment project design (# samples depth of read coverage) read mapping SNP calling structural variation discovery

21. SV discovery in autism deletion amplification

22. Lab

23. People

24. Resources computer cluster (72 servers) 128 GB RAM server ~200TB disk space 2 R01 grants (NHGRI/NIH) 1 R21 grant (NIAID/NIH) a BC RIG grant 2 RC2 grants (NHGRI/NIH) starting September 2009

25. Collaborations Baylor HGSC Wash. U. GSC Genome Canada UBC GSC Cornell UC Davis UCSF NCBI @ NIHNCI @ NIHMarshfield Clinic UCLA Pfizer

26. Graduate student rotations Looking for new graduate students Spots are available for all three rotations Lots or projects Caveat: you need to be able to program… Check us out at: http://bioinformatics.bc.edu/marthlab/ http://bioinformatics.bc.edu/marthlab/ If you are interested, please talk to me